
from google.colab import drive
drive.mount('/content/drive')

video_path = '/content/drive/MyDrive/ExtractedFrames/parking - 12 sept, 12pm.MP4'

Mounted at /content/drive


%pip install ultralytics supervision roboflow
import ultralytics
ultralytics.checks()

Ultralytics 8.3.24 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)
Setup complete ✅ (8 CPUs, 51.0 GB RAM, 32.6/235.7 GB disk)


from ultralytics import YOLO
model = YOLO("/content/drive/MyDrive/yolo11n.pt")  # load a pretrained model


# Mount Google Drive
from google.colab import drive
drive.mount('/content/drive')

Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).


#No specific class is specified
results = model.predict(source="/content/drive/MyDrive/ExtractedFrames/ExtractedFrames/ExtractedFrames12PM/frame_10110.jpg", save=True)
results = model.predict(source="/content/drive/MyDrive/ExtractedFrames/ExtractedFrames/ExtractedFrames12PM/frame_14220.jpg", save=True)
results = model.predict(source="/content/drive/MyDrive/ExtractedFrames/ExtractedFrames/ExtractedFrames7AM/frame7am_21060.jpg", save=True)

image 1/1 /content/drive/MyDrive/ExtractedFrames/ExtractedFrames/ExtractedFrames12PM/frame_10110.jpg: 384x640 18 cell phones, 62.5ms
Speed: 5.0ms preprocess, 62.5ms inference, 2.4ms postprocess per image at shape (1, 3, 384, 640)
Results saved to runs/detect/predict

image 1/1 /content/drive/MyDrive/ExtractedFrames/ExtractedFrames/ExtractedFrames12PM/frame_14220.jpg: 384x640 2 cars, 2 trucks, 8.7ms
Speed: 2.0ms preprocess, 8.7ms inference, 1.3ms postprocess per image at shape (1, 3, 384, 640)
Results saved to runs/detect/predict

image 1/1 /content/drive/MyDrive/ExtractedFrames/ExtractedFrames/ExtractedFrames7AM/frame7am_21060.jpg: 384x640 3 cars, 1 truck, 1 traffic light, 9.6ms
Speed: 2.5ms preprocess, 9.6ms inference, 1.2ms postprocess per image at shape (1, 3, 384, 640)
Results saved to runs/detect/predict


from IPython.display import Image as IPyImage
IPyImage(filename='/content/runs/detect/predict/frame_10110.jpg', width=600)


IPyImage(filename='/content/runs/detect/predict/frame_14220.jpg', width=600)


IPyImage(filename='/content/runs/detect/predict/frame7am_21060.jpg', width=600)


!pip install roboflow

from roboflow import Roboflow
rf = Roboflow(api_key="sXI0YRivvv6TyxnVzhoi")
project = rf.workspace("project-xv6fp").project("cartopview")
version = project.version(7)
dataset = version.download("yolov11")

Requirement already satisfied: roboflow in /usr/local/lib/python3.10/dist-packages (1.1.48)
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Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->roboflow) (3.3.2)
loading Roboflow workspace...
loading Roboflow project...

Downloading Dataset Version Zip in CarTopView-7 to yolov11:: 100%|██████████| 686358/686358 [00:32<00:00, 20904.03it/s]

Extracting Dataset Version Zip to CarTopView-7 in yolov11:: 100%|██████████| 5154/5154 [00:01<00:00, 2637.38it/s]


from sklearn.model_selection import GridSearchCV
from sklearn.base import BaseEstimator
from ultralytics import YOLO
import numpy as np

class YOLOEstimator(BaseEstimator):
    def __init__(self, lr=0.01, epochs=10):
        self.lr = lr
        self.epochs = epochs

    def fit(self, X=None, y=None):
        model = YOLO('yolo11n.pt')  # Initialize YOLOv8 model
        self.results = model.train(
            data='/content/CarTopView-7/data.yaml',
            epochs=self.epochs,
            optimizer='AdamW',
            lr0=self.lr,
            patience = 15,
            plots=True,
            save=True,
            project = '/content/drive/MyDrive/parkingGridsearch',
        )

    def score(self, X=None, y=None):
        # Use validation mAP or another metric as the scoring value
        return self.results['metrics/val_mAP_0.5']

# Define the parameter grid
param_grid = {
    'lr': [0.001, 0.01, 0.1],   # 3 values for learning rate
    'epochs': [10, 50, 100]        # 3 values for epochs
}


# Dummy data to pass to the grid search
X_dummy = np.zeros((10, 1))
y_dummy = np.zeros(10)

# Initialize and run GridSearchCV
grid_search = GridSearchCV(YOLOEstimator(), param_grid, scoring='accuracy', cv=3)
grid_search.fit(X=X_dummy, y=y_dummy)

Ultralytics 8.3.23 🚀 Python-3.10.12 torch-2.4.0+cpu CPU (Intel Xeon 2.00GHz)
engine/trainer: task=detect, mode=train, model=yolov8n.pt, data=/content/CarTopView-7/data.yaml, epochs=10, time=None, patience=15, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=/content/drive/MyDrive/parkingGridsearch, name=train19, exist_ok=False, pretrained=True, optimizer=AdamW, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=4, nms=False, lr0=0.001, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=/content/drive/MyDrive/parkingGridsearch/train19
Overriding model.yaml nc=80 with nc=3

                   from  n    params  module                                       arguments                     
  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 
  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                
  2                  -1  1      7360  ultralytics.nn.modules.block.C2f             [32, 32, 1, True]             
  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                
  4                  -1  2     49664  ultralytics.nn.modules.block.C2f             [64, 64, 2, True]             
  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               
  6                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]           
  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              
  8                  -1  1    460288  ultralytics.nn.modules.block.C2f             [256, 256, 1, True]           
  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 
 10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 11             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 12                  -1  1    148224  ultralytics.nn.modules.block.C2f             [384, 128, 1]                 
 13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
 14             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 15                  -1  1     37248  ultralytics.nn.modules.block.C2f             [192, 64, 1]                  
 16                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                
 17            [-1, 12]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 18                  -1  1    123648  ultralytics.nn.modules.block.C2f             [192, 128, 1]                 
 19                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 2]              
 20             [-1, 9]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 21                  -1  1    493056  ultralytics.nn.modules.block.C2f             [384, 256, 1]                 
 22        [15, 18, 21]  1    751897  ultralytics.nn.modules.head.Detect           [3, [64, 128, 256]]           
Model summary: 225 layers, 3,011,433 parameters, 3,011,417 gradients, 8.2 GFLOPs

Transferred 319/355 items from pretrained weights
TensorBoard: Start with 'tensorboard --logdir /content/drive/MyDrive/parkingGridsearch/train19', view at http://localhost:6006/
Freezing layer 'model.22.dfl.conv.weight'

train: Scanning /content/CarTopView-7/train/labels... 2406 images, 0 backgrounds, 0 corrupt: 100%|██████████| 2406/2406 [00:02<00:00, 1050.76it/s]

train: New cache created: /content/CarTopView-7/train/labels.cache

val: Scanning /content/CarTopView-7/valid/labels.cache... 109 images, 0 backgrounds, 0 corrupt: 100%|██████████| 109/109 [00:00<?, ?it/s]

Plotting labels to /content/drive/MyDrive/parkingGridsearch/train19/labels.jpg...

optimizer: AdamW(lr=0.001, momentum=0.937) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)
TensorBoard: model graph visualization added ✅
Image sizes 640 train, 640 val
Using 0 dataloader workers
Logging results to /content/drive/MyDrive/parkingGridsearch/train19
Starting training for 10 epochs...
Closing dataloader mosaic

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size

       1/10         0G      1.098      1.242      1.001        177        640: 100%|██████████| 151/151 [04:38<00:00,  1.85s/it]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 4/4 [00:04<00:00,  1.13s/it]

                   all        109       4023      0.916      0.871      0.952      0.751
Ultralytics 8.3.23 🚀 Python-3.10.12 torch-2.4.0+cpu CPU (Intel Xeon 2.00GHz)
engine/trainer: task=detect, mode=train, model=yolov8n.pt, data=/content/CarTopView-7/data.yaml, epochs=10, time=None, patience=15, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=/content/drive/MyDrive/parkingGridsearch, name=train2, exist_ok=False, pretrained=True, optimizer=AdamW, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=4, nms=False, lr0=0.001, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, label_smoothing=0.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=/content/drive/MyDrive/parkingGridsearch/train2
Overriding model.yaml nc=80 with nc=3

                   from  n    params  module                                       arguments                     
  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 
  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                
  2                  -1  1      7360  ultralytics.nn.modules.block.C2f             [32, 32, 1, True]             
  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                
  4                  -1  2     49664  ultralytics.nn.modules.block.C2f             [64, 64, 2, True]             
  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               
  6                  -1  2    197632  ultralytics.nn.modules.block.C2f             [128, 128, 2, True]


from ultralytics import YOLO
import os
import yaml
from IPython.display import display
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

# Base directory containing the train folders
base_dir = '/content/drive/MyDrive/parkingGridsearch'

# Loop through folders from train2 to train10
for i in range(2, 11):
    folder_path = os.path.join(base_dir, f'train{i}')

    # Load hyperparameters from args.yaml
    args_path = os.path.join(folder_path, 'args.yaml')
    if os.path.exists(args_path):
        with open(args_path, 'r') as file:
            args = yaml.safe_load(file)
        lr0 = args.get('lr0', 'N/A')
        epochs = args.get('epochs', 'N/A')
        print(f"Model in train{i} was trained with initial lr0={lr0} and epochs={epochs}")
    else:
        print(f"args.yaml not found in train{i}")
        continue

    # Load and evaluate the model
    model_path = os.path.join(folder_path, 'weights/best.pt')
    model = YOLO(model_path)
    results = model.val()

    # Extract and print specific evaluation metrics
    map_50 = results.box.map50  # mAP at IoU threshold 0.5
    map_95 = results.box.map    # mAP at IoU threshold 0.5:0.95

    print(f"Evaluation results for model in train{i}:")
    print(f"mAP@0.5: {map_50:.4f}")
    print(f"mAP@0.5:0.95: {map_95:.4f}")
    print("=" * 50)

    # Display results.png
    results_png = os.path.join(folder_path, 'results.png')
    if os.path.exists(results_png):
        print(f"Displaying results.png for train{i}:")
        display(Image(filename=results_png))
    else:
        print(f"results.png not found in train{i}")

    # Display val_batch2_labels.jpg as "Ground Truth" and val_batch2_pred.jpg as "Predicted" side by side
    labels_image_path = os.path.join(folder_path, 'val_batch2_labels.jpg')
    pred_image_path = os.path.join(folder_path, 'val_batch2_pred.jpg')

    if os.path.exists(labels_image_path) and os.path.exists(pred_image_path):
        print(f"Displaying Ground Truth and Predicted images for train{i}:")

        # Set up the side-by-side plot
        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 9))

        # Load and display each image
        img_labels = mpimg.imread(labels_image_path)
        img_pred = mpimg.imread(pred_image_path)

        ax1.imshow(img_labels)
        ax1.set_title('Ground Truth')
        ax1.axis('off')

        ax2.imshow(img_pred)
        ax2.set_title('Predicted')
        ax2.axis('off')

        plt.show()
    else:
        print(f"val_batch2_labels.jpg or val_batch2_pred.jpg not found in train{i}")

    print("=" * 100)  # Separator line for readability

Model in train2 was trained with initial lr0=0.001 and epochs=10
Ultralytics 8.3.24 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)
Model summary (fused): 168 layers, 3,006,233 parameters, 0 gradients, 8.1 GFLOPs

val: Scanning /content/CarTopView-7/valid/labels.cache... 109 images, 0 backgrounds, 0 corrupt: 100%|██████████| 109/109 [00:00<?, ?it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:07<00:00,  1.06s/it]

                   all        109       4023      0.984      0.973      0.991      0.866
                   Car        103       1680      0.976      0.962      0.987      0.831
              Disabled         71        277      0.985      0.975      0.992      0.852
                 Empty         98       2066       0.99      0.983      0.994      0.915
Speed: 1.1ms preprocess, 6.9ms inference, 0.1ms loss, 37.8ms postprocess per image
Results saved to runs/detect/val25
Evaluation results for model in train2:
mAP@0.5: 0.9909
mAP@0.5:0.95: 0.8660
==================================================
Displaying results.png for train2:

Displaying Ground Truth and Predicted images for train2:

====================================================================================================
Model in train3 was trained with initial lr0=0.001 and epochs=10
Ultralytics 8.3.24 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)
Model summary (fused): 168 layers, 3,006,233 parameters, 0 gradients, 8.1 GFLOPs

val: Scanning /content/CarTopView-7/valid/labels.cache... 109 images, 0 backgrounds, 0 corrupt: 100%|██████████| 109/109 [00:00<?, ?it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:07<00:00,  1.10s/it]

                   all        109       4023      0.984      0.973      0.991      0.866
                   Car        103       1680      0.976      0.962      0.987      0.831
              Disabled         71        277      0.985      0.975      0.992      0.852
                 Empty         98       2066       0.99      0.983      0.994      0.915
Speed: 1.3ms preprocess, 3.2ms inference, 0.0ms loss, 42.7ms postprocess per image
Results saved to runs/detect/val26
Evaluation results for model in train3:
mAP@0.5: 0.9909
mAP@0.5:0.95: 0.8660
==================================================
Displaying results.png for train3:

Displaying Ground Truth and Predicted images for train3:

====================================================================================================
Model in train4 was trained with initial lr0=0.01 and epochs=10
Ultralytics 8.3.24 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)
Model summary (fused): 168 layers, 3,006,233 parameters, 0 gradients, 8.1 GFLOPs

val: Scanning /content/CarTopView-7/valid/labels.cache... 109 images, 0 backgrounds, 0 corrupt: 100%|██████████| 109/109 [00:00<?, ?it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:07<00:00,  1.06s/it]


from ultralytics import YOLO
import os
import yaml
from IPython.display import display
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

# Base directory containing the train folders
base_dir = '/content/drive/MyDrive/parkingGridsearch'

# Loop through folders from train2 to train10
for i in range(2, 11):
    folder_path = os.path.join(base_dir, f'train{i}')

    # Load hyperparameters from args.yaml
    args_path = os.path.join(folder_path, 'args.yaml')
    if os.path.exists(args_path):
        with open(args_path, 'r') as file:
            args = yaml.safe_load(file)
        lr0 = args.get('lr0', 'N/A')
        epochs = args.get('epochs', 'N/A')
        print(f"Model in train{i} was trained with initial lr0={lr0} and epochs={epochs}")
    else:
        print(f"args.yaml not found in train{i}")
        continue

    # Load and evaluate the model
    model_path = os.path.join(folder_path, 'weights/best.pt')
    model = YOLO(model_path)
    results = model.val(conf=0.5)  # Set validation threshold

    # Extract and print specific evaluation metrics
    map_50 = results.box.map50  # mAP at IoU threshold 0.5
    map_95 = results.box.map    # mAP at IoU threshold 0.5:0.95

    print(f"Evaluation results for model in train{i}:")
    print(f"mAP@0.5: {map_50:.4f}")
    print(f"mAP@0.5:0.95: {map_95:.4f}")
    print("=" * 50)

    # Display results.png
    results_png = os.path.join(folder_path, 'results.png')
    if os.path.exists(results_png):
        print(f"Displaying results.png for train{i}:")
        display(Image(filename=results_png))
    else:
        print(f"results.png not found in train{i}")

    # Display val_batch2_labels.jpg as "Ground Truth" and val_batch2_pred.jpg as "Predicted" side by side
    labels_image_path = os.path.join(folder_path, 'val_batch2_labels.jpg')
    pred_image_path = os.path.join(folder_path, 'val_batch2_pred.jpg')

    if os.path.exists(labels_image_path) and os.path.exists(pred_image_path):
        print(f"Displaying Ground Truth and Predicted images for train{i}:")

        # Set up the side-by-side plot
        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 9))

        # Load and display each image
        img_labels = mpimg.imread(labels_image_path)
        img_pred = mpimg.imread(pred_image_path)

        ax1.imshow(img_labels)
        ax1.set_title('Ground Truth')
        ax1.axis('off')

        ax2.imshow(img_pred)
        ax2.set_title('Predicted')
        ax2.axis('off')

        plt.show()
    else:
        print(f"val_batch2_labels.jpg or val_batch2_pred.jpg not found in train{i}")

    print("=" * 100)  # Separator line for readability

Model in train2 was trained with initial lr0=0.001 and epochs=10
Ultralytics 8.3.24 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)
Model summary (fused): 168 layers, 3,006,233 parameters, 0 gradients, 8.1 GFLOPs

val: Scanning /content/CarTopView-7/valid/labels.cache... 109 images, 0 backgrounds, 0 corrupt: 100%|██████████| 109/109 [00:00<?, ?it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:07<00:00,  1.06s/it]

                   all        109       4023      0.991      0.967      0.982      0.879
                   Car        103       1680      0.985      0.949      0.973      0.848
              Disabled         71        277      0.993      0.975      0.987      0.863
                 Empty         98       2066      0.995      0.977      0.987      0.926
Speed: 1.4ms preprocess, 14.3ms inference, 0.0ms loss, 18.6ms postprocess per image
Results saved to runs/detect/val34
Evaluation results for model in train2:
mAP@0.5: 0.9825
mAP@0.5:0.95: 0.8789
==================================================
Displaying results.png for train2:

Displaying Ground Truth and Predicted images for train2:

====================================================================================================
Model in train3 was trained with initial lr0=0.001 and epochs=10
Ultralytics 8.3.24 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)
Model summary (fused): 168 layers, 3,006,233 parameters, 0 gradients, 8.1 GFLOPs

val: Scanning /content/CarTopView-7/valid/labels.cache... 109 images, 0 backgrounds, 0 corrupt: 100%|██████████| 109/109 [00:00<?, ?it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:07<00:00,  1.07s/it]

                   all        109       4023      0.991      0.967      0.982      0.879
                   Car        103       1680      0.985      0.949      0.973      0.848
              Disabled         71        277      0.993      0.975      0.987      0.863
                 Empty         98       2066      0.995      0.977      0.987      0.926
Speed: 1.4ms preprocess, 3.2ms inference, 0.0ms loss, 40.8ms postprocess per image
Results saved to runs/detect/val35
Evaluation results for model in train3:
mAP@0.5: 0.9825
mAP@0.5:0.95: 0.8789
==================================================
Displaying results.png for train3:

Displaying Ground Truth and Predicted images for train3:

====================================================================================================
Model in train4 was trained with initial lr0=0.01 and epochs=10
Ultralytics 8.3.24 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)
Model summary (fused): 168 layers, 3,006,233 parameters, 0 gradients, 8.1 GFLOPs

val: Scanning /content/CarTopView-7/valid/labels.cache... 109 images, 0 backgrounds, 0 corrupt: 100%|██████████| 109/109 [00:00<?, ?it/s]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 7/7 [00:07<00:00,  1.04s/it]


model = YOLO('/content/drive/MyDrive/parkingGridsearch/train10/weights/best.pt')
results = model.predict(source="/content/drive/MyDrive/ExtractedFrames/ExtractedFrames/ExtractedFrames12PM/frame_3420.jpg", save=True)

image 1/1 /content/drive/MyDrive/ExtractedFrames/ExtractedFrames/ExtractedFrames12PM/frame_3420.jpg: 384x640 35 Cars, 8 Emptys, 8.2ms
Speed: 2.3ms preprocess, 8.2ms inference, 1.6ms postprocess per image at shape (1, 3, 384, 640)
Results saved to runs/detect/predict4


from IPython.display import Image as IPyImage
IPyImage(filename='/content/runs/detect/predict4/frame_3420.jpg', width=600)


results = model.predict(source="/content/drive/MyDrive/ExtractedFrames/ExtractedFrames/ExtractedFrames12PM/frame_120.jpg", save=True)

image 1/1 /content/drive/MyDrive/ExtractedFrames/ExtractedFrames/ExtractedFrames12PM/frame_120.jpg: 384x640 43 Cars, 4 Emptys, 7.7ms
Speed: 2.3ms preprocess, 7.7ms inference, 1.6ms postprocess per image at shape (1, 3, 384, 640)
Results saved to runs/detect/predict4


IPyImage(filename='/content/runs/detect/predict4/frame_120.jpg', width=600)


# Citations and Acknowledgments

# @software{yolo11_ultralytics,
#   author = {Glenn Jocher and Jing Qiu},
#   title = {Ultralytics YOLO11},
#   version = {11.0.0},
#   year = {2024},
#   url = {https://github.com/ultralytics/ultralytics},
#   orcid = {0000-0001-5950-6979, 0000-0002-7603-6750, 0000-0003-3783-7069},
#   license = {AGPL-3.0}
# }

Evaluating YOLO11 Model for Object Detection in Drone Footage¶

Pretrained YOLOv11n¶

What is next?¶

Grid Search - YOLO¶

Grid Search training results¶

Test our model on frames of our dataset¶